Our work focuses on the Multi-Object Navigation (MultiON) task, where an
agent needs to navigate to multiple objects in a given sequence. We
systematically investigate the inherent modularity of this task by dividing our
approach to contain four modules: (a) an object detection module trained to
identify objects from RGB images, (b) a map building module to build a semantic
map of the observed objects, (c) an exploration module enabling the agent to
explore its surroundings, and finally (d) a navigation module to move to
identified target objects. We focus on the navigation and the exploration
modules in this work. We show that we can effectively leverage a PointGoal
navigation model in the MultiON task instead of learning to navigate from
scratch. Our experiments show that a PointGoal agent-based navigation module
outperforms analytical path planning on the MultiON task. We also compare
exploration strategies and surprisingly find that a random exploration strategy
significantly outperforms more advanced exploration methods. We additionally
create MultiON 2.0, a new large-scale dataset as a test-bed for our approach.

本文研究了多目标导航任务，通过四个模块（物体检测、语义地图构建、环境探索和导航）来解决此任务。结果表明，在 MultiON 任务中，采用 PointGoal 导航模型优于从头开始学习导航。与分析路径规划相比，基于 Agent 的导航模块表现更好。同时也探讨了探索策略并发现随机探索策略显著优于更高级的探索方法。我们还创建了 MultiON 2.0 数据集作为我们方法的测试平台。